Predictive analytic method and apparatus

ABSTRACT

A computerized project management analytical system and method that develops and manages an ontology that links objects and is capable of being mined. The ontology is comprised of a project ontology framework, a matching engine and a project status matrix that illustrates a multi-relational view of the project status, of confidence levels, or interdiction points and/or positions on project timelines.

This application claims benefit to U.S. Provisional Application60/642,983 filed Jan. 12, 2005 which is herein incorporated byreference.

FIELD OF THE INVENTION

The invention relates to a computer-based project assessment tool forschematically matching information into a project scheme.

BACKGROUND OF THE INVENTION

Today, in project management, the focus of analysis and control is onthe ability to estimate and associate what is effectively remembered asimportant with a given project. In other words, since seventy percent ofall projects fail based on their original budget or finish date, it isclear that current systems struggle with successful estimations foroutcomes. Part of this failure to predict, analyze and control projectoutcome stems from the inability to effectively mine and place into theproper context the avalanche of the data that could positively improvethe predictive outcome of the project.

Project management, search software, data mining software andstatistical/analytical tools could be used resolve project managementshortfalls. However, these various tools exist in their own silos andare thereby not associated in a meaningful and usable manner. Thisfailure is exacerbated as the complexity of projects increases astechnology and society evolve.

Moreover, the concept of a project for many human endeavors is becomingwidespread and mutating so that increasingly sophisticated tools, ifapplied correctly, could be implemented in more wide-rangingenvironments. For example, tools could be used in different ways,depending on the wide range of possibilities of what constitutes “aproject”, and who is the “project manager”. For example, an individualplanning threatening activities could be a deemed a “project manager” inthe same way a more traditional individual, such as a certified projectengineer, could plan a construction, research or information technologyproject. Other environments that rely heavily on project management andcontrol and that could benefit from a more sophisticated analyticalapproach to project management include but are not limited to the filmindustry, the automotive industry, advertising, drug/pharmaceuticalresearch, clinical medical trials, to name a few.

A need therefore exists in the art for a predictive analytic system andmethod that employs the best available software tools and that run onstandard computer hardware in order to provide project predictiveanalytics to the end user.

SUMMARY OF THE INVENTION

The above-described deficiencies are overcome by a system and methodadapted for use on a computer platform that provides an ontology thatlinks objects and is capable of being mined. The ontology is comprisedof a project ontology framework, a matching engine and a project statusmatrix that illustrates a multi-relational view of the project status,of confidence levels, or interdiction points and/or positions on projecttimelines.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an exemplary project ontology framework according tothe present invention;

FIG. 2 illustrates an exemplary mapping engine according to the presentinvention;

FIG. 3 illustrates a block diagram of an Echo State Network;

FIGS. 4A and 4B respectively illustrate exemplary display statusmatrices according to the present invention;

FIG. 5 is a flow chart illustrating the preferred embodiment of theinvention; and

FIG. 6 is an example of a project ontology for the aerospace industry.

DETAILED DESCRIPTION OF THE INVENTION

The present invention described in the following specification and inthe attached drawings wherein like elements are referenced to likereference numerals.

This invention is adapted for use on a host computer platform includingon a personal computer, on a server, on a website, on a local or widearea network, on a PDA or on any other processor-based device known orused in the art.

The assessment tool or ontology of the present invention is an explicitformal specification of how to represent objects, concepts and otherentities that are assumed to exist in an area of interest. The ontologylinks the objects and concepts with the relationships among the objectsand concepts. The premise is based on the fact that today'sorganizations, whether government, commercial or otherwise, can be seenas project management specialists that oversee a diverse portfolio ofrelated projects and these projects may share similarities.

Not only does the ontology mine data but it provides current status,while making predictions about future status. In its broadest sense, theontology project framework uses a template for searching data. Insteadof gathering data piece by piece, the template can encompass a wholedata set seamlessly.

Specification tools, neural network technology and natural languageprocessing are needed to create an effective ontology. The ontology usesspecification tools, such as a Resource Definition Framework (RDF) andan Ontology Web Language (OWL), which allow complete areas of knowledgeto be machine processed. Neural networks, such as Echo State Networks(ESN), simplify machine processing while increasing accuracy. Inaddition, powerful lexical dictionaries, such as WordNet® allow moreaccurate statistical natural language processing (NLP).

Objects and concepts are mined in projects. Any ontology may include aproject portfolio. As will be described in the present specification,the system is set up to map or mine information to a particular projector activity within the ontology. The ontology matching scheme describedherein has been contemplated in a number of different implementationapplications. For example, the ontology can be used to predictthreatening activities or ontology “projects” for project management ofimportant projects or even strategic initiatives in a corporateenvironment.

The ontology can be used for any application where there are relevantprojects. For example, the ontology is used to predict threats. Thisimplementation of the ontology is logical because organizations planningthreatening acts behave in a hierarchical structure. In the ontology,the actions or acts that organizations carry out are projects. Usingthis structure and additional intelligence mapped into the ontologystructure, projects that involve threats can be predicted by analyzingthe recent intelligence in conjunction with the threat-oriented projectplan templates or maps. In an intelligence analysis perspective, theontology allows systematic categorization of knowledge concerning anorganization, a learning neural network of threat based projects, highconfidence predictions of project status, and threat warning andintervention.

Another example is an ontology that is implemented in a commercialsetting. In one example, email, instant messaging and even computerkeystrokes could be used to feed data into the ontology scheme. Theemail or other communication messages are used by employees to discusswork activities and the progress of such activities. The casualcommunication between employees is invaluable to the business. Bytracking and using the communications, the progress of the strategicinitiative and important projects can be predicted. The prediction ismade by analyzing the corporate communication, against strategicalignment or project template plans. In the commercial setting, theontology allows real time project status from mined documents, e-mail,instant messaging, and other project related data repositories, highconfidence scenarios for risk management, and greater success in largecomplex projects. These implementation examples may be used throughoutthe specification to provide a context for the ontology scheme.

The ontology is comprised of three major components which are describedin further detail in the attached figures. The project ontologyframework (POF) is the first component. The project ontology includestemplate projects, discrete project activities, project roles, projecttemplate lexicons, project template lexicon networks, sponsororganization lexicons, sponsor organization social networks, sponsororganization lexicon networks, inter sponsor organization socialnetworks, inter sponsor organizational lexicon networks, projecttemplate activity networks, inter project template activity networks,sponsor organization project portfolios, etc. The POF is a set ofmethods for constructing a project ontology.

The second component is the matching engine. The matching engineprovides a method of associating a given piece of data with a discreteproject activity or project template.

The third component of the ontology based system is a project statusmatrix that provides a multi-relational view display of a sponsoringorganization by project templates, current assessment matching to showcurrent status, confidence levels, interdiction points, and position onthe project timeline.

The ontology schematic matching system is constructed using a machinereadable language (e.g., OWL). OWL, for example, is a specificationpublished by the world wide web consortium (w3c.org). OWL is designed tobe used in those environments where the content of information is beingprocessed, not just presented. OWL allows for improved machineinterpretability of Web content by providing additional vocabulary alongwith formal semantics. OWL has three increasingly expressivesublanguages, including OWL Lite, OWL DL and OWL Full.

Project Ontology Framework (POF)

Each POF is constructed of five major classes and their subclasses. Theontology base framework is created from expert input, historical data,what-if exercises, analysis of other ontology states, as well ascreative brainstorming. The classes include lexicon, portfolios, projecttemplates, activity and role. Each of these classes has relationshipties to other classes by a class value. FIG. 1 illustrates an exemplaryPOF.

The first class of the POF 182 is the lexicon 12. The lexicon class 12provides a knowledge base about a subset of words in the vocabulary of anatural language. The lexicon subclasses includes the WordNet® database,and entity and specialized lexicons mapped to the WordNet® database. Theorganization and specialized lexicons are appropriately linked to theclasses to which the particularized lexicons apply through lexiconnetworks. Additionally, the lexicon class 12 includes lexicon networkswhich are networks/matrices of words, by use, networkedgrammatically/cognitively. Lexicon networks are constructed at theportfolio level as well as per project template, activity and role.

WordNet® is an open source application developed by and made availablethrough Princeton University. In WorldNet®, nouns, verbs, adjectives,adverbs, etc., are organized into sets, each set representing oneunderlying lexical concept. The word sets are linked by differentrelations. Although, this invention is contemplated using WordNet®software, other lexical reference systems or tools inspired bypsycholinguistic theories of human lexical memory can be used to developthe Lexicon.

The second class of the POF 182 is the portfolio 14. The portfolio 14includes the subclasses of sponsor(s), projects (templates and ongoingprojects), roles and lexicons not clearly associated with a project oran activity, related portfolios and other metadata. Role and activityinformation not clearly associated with a project or activity is kept inthe portfolio structure 182 for later use.

The third class of the POF 182 is the project template 16. The projecttemplate 16 is made up of several subclasses. The first subclassdelineates the sponsor organization. The sponsor organization is thegroup which is carrying out a particular activity (e.g., Hamas, acorporate competitor, an organization). Activities of the project 16make up the second subclass. The activities are the different activitiesthat are being carried out or need to be carried out to complete theproject. The subclasses also include lexicons and roles that are notclearly associated with an activity. By maintaining this information, ifat a later point the associated activity becomes clear, the data may bemapped to the appropriate activity. Additionally, the project templatesallow for information relating to other related projects. The finalsubclass of the project templates is the other metadata.

The fourth class in the POF 182 is activities 18. The activity class 18is comprised of verbs, nouns, adjectives, adverbs and roles. Additionalsubclasses include a time sequence of events or actions, relatedactivities and other metadata.

The final class in the POF is the role 20. The role class 20 iscomprised of the subclasses of skills, functions, commandrelationship(s) (organizational chart level), tools, named individualsacting in this role, related roles, and other metadata.

The base POF 182 is the complete POF structure without any mapping ofdata. It is possible that the classes of the base POF 182 can changedepending on changes to the relationship data and to the measuredactivity. The base POF 182 is continuously evolving as new relationshipdata is added and the fidelity in the base POF 182 increases as newrelationship data is added. Changes to the base POF 182 are based on theguiding configuration management principals, policies and thresholds. Byadhering to guiding configuration management principles, the base POF182 cannot be changed at the whim of a user.

The POF 182 is a large network of relationships codified in machinereadable language. Real time instances of the base POF 182 are createdby combining a recurrent neural network algorithm (RNN), such as theEcho State Network (ESN) 206 (illustrated in FIG. 3), with the base POF182. As data is input into the ontology, the RNN algorithm is constantlygenerating echoes of the base POF 182. Based on weight (w) andconfidence cutoffs, certain echoes are captured.

There are three basic types of non-base POFs: candidate, working andoperational POFs (see FIG. 2). The operational POF is the only POF thatis used for status display and base POF refinement. The operational POFalso represents the highest confidence echo based on the data that ismapped to the POFs. Information in the operational POF can and often isused to refine the base POF. The difference between candidate, workingand operational POF is based on confidence cutoffs. A working POF doesnot replace the operational POF unless a user decides that it is a moreaccurate view of the POF. Additionally, because the POF status is basedon confidence cutoffs, not every echo will rise to the level of aworking POF.

However, echoes that do not rise to the level of working POFs andworking POFs can be used later to backtrack and determine if informationthat has been mapped to a project, role, activity, etc., is stillaccurate.

Mapping Engine/Data Receipt & Routing

Referring now to FIG. 2, one of the main features of the ontology is amapping engine 100. Regardless of where the mapping engine 100 mapsintelligence data, the actions of the engine 100 remain generally thesame. An exception is in the fidelity of the map. The mapping engine 100carries out six functions, as illustrated in FIG. 2. These six functionsinclude data receipt 102, routing 104, parsing and formatting 106,mapping 108, echo generation (state) 110 and echo maintenance 112.

The mapping engine 100 first classifies at 120 incoming intelligencebased on the source of the information at 122. The source of theintelligence/data is used to determine which pathway of algorithms thedata will travel in preparation for mapping. For example, unstructureddata will undergo statistical Natural Language Processing (NLP), whilemachine-tagged data will go to a transformation function prior tomapping. The data will be processed into the proper RDF/OWL formatbefore mapping.

The source of the data is used to create message types 122. Exemplarysources of data may include the internet, workflow, email, instantmessaging, a document management server, or a project server. Eachmessage type is governed by a rules engine 124 that provides subsequentprocessing. Processing can include, but is not limited to, evaluation ofthe source and message formatting (e.g., formatting the message into theproper OWL/RDF format). The rules engine 124 may also provide an initialscoring of the message. The confidence levels assigned throughout thePOF ontology will be aggregated to classify the POFs as candidate 160,working 162 and operational 164 POFs. A confidence level is assignedbased on the source of the data. The rules engine 124 is in effect, aseries of software agents that reside near the repository or source of amessage. Aggregate confidence levels are used in echo generation todetermine which POF's are candidate, working and operational 160, 162and 164 respectively.

Messages are classified into types, as illustrated in FIG. 2. Themessage types include “trusted parsed directed” 130, “trusted parsedprocess” 132, “assigned confidence” 126, “no assigned confidence” 134and “fragment” 136.

The type of intelligence with the highest confidence type is “trustedparsed directed” 130. This message type is from a very high confidencesource (e.g., workflow). The data is already parsed using WordNet®, isin the proper RDF/OWL format, and is directed via routing 142 to aspecific area of the ontology. The message is directed to a specificplace in the ontology by an end user or is automatically directedbecause of the source. Optionally, the message is created by an analystbased on multiple sources to provide a finished mapping of data directlyto the highest confidence working POF or all of the POFs. This messagetype bypasses the parsing 106 and mapping 108 functions of the mappingengine 100.

The second message type is “trusted parsed process” 132. This messagetype is from a high confidence source, usually the end user. The messagehas already been parsed using WordNet®, is in the proper RDF/OWL format,and is sent directly through routing module 144 to the mapping engine108, skipping the parsing function 106.

The third message type is “assigned confidence” 126. This message typeis assigned a confidence route 140 by source or end user. However, it isa raw piece of data without parsing 106 or formatting (e.g., internetemail). The message will proceed through 140 to the parsing function106, as illustrated in FIG. 2, to be processed as described in moredetail below.

The fourth message type “no assigned confidence” 134 is a raw piece ofdata without parsing 106 or formatting, but the source is known.Further, whether by source or the end user's direction, the data doesnot have an assigned confidence level 146. Subsequent processing will beused to determine the confidence level. The message will proceed throughrouting module 146 to the parsing function 106, as illustrated in FIG.2.

The last message type, fragment 136, is also raw data without parsing106 or formatting. However, the source of the data is unknown. Fragmentdata is weighted lower 148 in the subsequent confidence level processingrouting. The message will proceed through routing module 148 thereforeto the parsing function 106 as illustrated in FIG. 2.

Based on the message type and configurable rules 124 of the ontologyframework, the data is routed either to the parsing and formatting 106function, the mapping function 108, or directly into an echo(es) states112.

FIG. 5 illustrates exemplary steps used for providing lexical processingas described with reference to the data receipt module 102 and routingmodule 104 in FIG. 2.

At 400, new data is provided to the mapping engine. That data is thentested at 402 to determine whether or not it matches the POF lexicon. Ifit matches at 405, then it is next determined whether the data isassociated with a project at 406. If there is no matching project at407, then the data is not entered and/or associated with the POF.

For data that is then successfully associated with a project 409, thatdata is then tested at 410 to determine whether or not there is anactivity that can be associated with that data. An example of a type ofactivity is a task.

Data that is not successfully associated with a project 411, at 408 thedata is associated with the POF. Also, data that is not associatedsuccessfully associated with an activity at 417 is associated with theidentified project at 414.

Referring back to 410, if data can be associated with a task at 413, thedata is then associated with all appropriate tasks and it is determinedwhether there is metadata to associate with the data. If matchingmetadata is available at 415, then the new data is also associated at418 with the metadata. Data that is not matched to metadata at 419, isthen associated with the tasks in step 416.

Mapping Engine—Parsing

The parsing function 106 uses statistical NLP to parse words within amessage. Words in a message are tagged 150 as to the part of speech,definition and time 154 (e.g., today, yesterday, Sep. 11, 2001, twoweeks, etc.).

The part of speech tagging 150 attempts to determine if a word is anoun, verb, determiner, adjective, adverb, pronoun, preposition,particle, conjunction, or number. In determining the part of speech,WordNet® Lookup, or other similar function in a comparable referencesystem, is performed for each of the words in a message. When WordNet®determines that the word has only one use (e.g., noun), the word is sentto sense processing 152. However, if a word has multiple parts ofspeech, sense determination 152 may play a deciding factor indetermining the word's part of speech.

Brill Tagger is an open source application developed by Eric Brill andmade available through www.cs.jhu.edu. A modified version of the opensource Brill Tagger can be used as one means of part-of-speech tagging150. Any other conventionally known speech tagging software or hardware,however, can be employed in conjunction with the present invention.Modified Brill Tagger is a modified version of the open source BrillTagger coupled with WordNet® and sponsor lexicons. Based on rulesassociated with the transformation process and the polysemy counts ofthe associated word, the tags are given a confidence number for laterevaluation. A tree traversal, statistical approach of WordNet® scoreseach part-of-speech for the words. A tree traversal traverses the chainof concepts (synonyms and related concepts) in the WordNet® databaselooking for common works in the message and the change of concepts todetermine the sense. The tree traversal processing also determines thesense of the word 152. Both the tag 150 and the sense 152 are scoredwith a confidence 156 for final processing.

Wording mapping, including tagging 150 and sense determination 152 worksas follows. The message is transformed into a series of networks byconnecting words that are used in the same sentence, phrase, and clause(i.e., creating a words in context network). A master network is createdby all of the sentence, phrase, and clause networks. Multiple uses ofthe same word in the message phrasing are depicted in the strength ofthe connection. The networks are actually stored as matrices of all ofthe words, as both row and column. The strength of connection betweenwords is signified as a number. The algorithm next looks to the existingsponsor, project, and activity lexicons, or other training corpuses, toseek a similar sentence, phrase, and clause matrix. The entire messagematrix is used to seek matching matrices in the project and activitylexicon matrices.

The highest confidence match is used to tag the part of speech 150, andselect the appropriate sense 152 for each word in the message. Aconfidence score is applied for later processing.

The confidence scores are used to de-conflict differences in tag andsense selections using a combination of voting and highest confidencewins process.

After the part of speech tagging 150 and sense determination 152 isfinished, each word is time context processed 154. Time contextprocessing is a specialized form of sense determination. Each word isevaluated to determine if it's sense is related to time. The senseprovided by the parsing function 106 is used to traverse through thechain of concepts in the WordNet® database or databases created bysoftware having similar features to WordNet. If time relatedwords/concepts are detected, an algorithm attempts to place the word ona continuous time-line. If possible, the word is given a distinct date,otherwise the word is given the appropriate level calendar fidelity.

Mapping and State/Echo Generation

Once part of speech tagging and sense selection is done for each word,the message is evaluated to extract and format the data according to themain areas of the ontology for mapping 108. The message is formattedaccording to the following roles at 180:

Portfolio Format—when more than one portfolio is present. LexiconFormat—to determine sponsor organization, project template, or activity.Project Template Format—to determine which project template. ActivityFormat—to determine the appropriate activity(ies) present in themessage. Role Format—to determine the appropriate role(s) present in themessage.

Each format allows the mapping process 108 or module 180 to step throughthe message from multiple perspectives. The mapping process 108 ormodule 180 generates an echo or state 110 for each format, as well asone encompassing all of the formats.

The reformatted message is input to at least one RNN algorithm, asillustrated in FIG. 3 (element 200) during the mapping module orprocess. Each project, activity, role, etc. may each have a RNNalgorithm to process the inputted message. Ideally, the RNN algorithmtests the input message against the base POF 182 (FIG. 2). Each itemwithin the POF has a threshold value for an output 184 to modify acandidate POF 160, working POF 162 and operational POF 164 status. Amessage may have only one high confidence match within the base POF 182,or it may have multiple matches (it may match multiple activities forexample). A high confidence match is user definable, but would generallybe considered as statistically significant. Once at least one match hasbeen made, at least a second module of process generates an echo orstate of the match 190 for each candidate 160, working 162, and theoperational POF 164. New overall scores are then calculated, and if thematch exceeds the candidate 160, or working 110 thresholds, or outscoresthe current operational POF, then the echoed state is promotedaccordingly by module or process 190 through output 184.

For example, if an activity format message is fed into the module orprocess 180, it is evaluated against all of the activity internals basedon match weight (see FIG. 3, element W). First, each activity word isevaluated to the activity lexicon including synonyms, antonyms,meronyms, holonyms, hypernyms, and sense. An aggregate score for eachword is assigned based on the probability of the activity word matchingthe activity lexicon in the base POF 182.

Next, the activity lexicon network/matrix of the message is evaluatedagainst the activity lexicon network/matrix in the base POF 182. Theactivity lexicon matrix is a networking of words that are linked from agrammatical and cognitive relationship. This evaluation is also given ascore. The score represents the statistical probability that the messageactivity matrix matches the activity matrix in the POF. From there, eachactivity attribute is evaluated against each activity in the POF (e.g.,time sequence, role, etc.) 190. If the overall score exceeds the matchweight, then the match moves to the second algorithm. As the base POF182 is refined, the match weights increase, thus increasing theconfidence level of each POF state 112.

Status Matrix

Referring now to FIGS. 4A & 4B, the project portfolio status matrixprovides a visual display of the operational POF. Visualization can beaccomplished on any known type of visualization hardware including butnot limited to a CRT, flat panel projection, LCD or LED matrix, etc.

FIGS. 4A and 4B illustrate exemplary status matrices according to thepresent invention. The status is displayed by the sponsoringorganization portfolio. The matrix provides a visualization of theprojects based on the POF confidence level, and possible interdictionopportunities. The timelines maybe depict the status of the projectsover a set period of time or may be adjustable for a specific time view.

FIG. 4A illustrates an exemplary status matrix 300 of the operationalPOFs that can exist in a commercial application. FIG. 4B illustrates anexemplary status matrix 350 of the operation POF that can exist if theproject is based on predicting threatening activities. The matrix canprovide information on projects that are jointly sponsored. The timelines may be depicted by a variety of colors to illustrate whichprojects are nearing completion or interdictions point, for example.

Referring to FIG. 4A, the vertical axis illustrates several typicalcommercial applications or types of projects 320 illustrated by thematrix 300. The horizontal matrix in this example shows differenttypical company offices or performing organizations 330 that relate toeach gant bar 336. In this example, the offices represented include thecompany CIO (chief information officer), company CMO (chief marketingofficer), company CFO (chief financial officer), and company CTO (chieftechnical officer).

The gant bar 335 is subdivided by time-based markers 336. The top marker324 illustrates current project status based on the time line. Theinternal markers 338 represent a high risk area for a company. At thispoint, this is typically deemed a point of weakness where projects aremost likely to fail or be delayed or suffer cost overruns.

In FIG. 4B, the same gant bar display 330 is applied to examples of thedifferent types of projects (e.g. vehicular bomb, biological andradiological) for different types of performing organizations 330 (e.g.Hamas, Al Qaeda, Islamic Jihad and Domestic). However, the interdictionpoint, or points 338, now represent areas where police or militaryintervention is optimal, and where these projects have their greatestweakness and exposure points.

FIG. 6 illustrates an exemplary project ontology, according to theafore-described invention, providing the intersection of two taxonomies.

The above description and accompanying figures are only illustrative ofexemplary embodiments that can achieve the features and advantages ofthe present invention. It is not intended that the invention be limitedto the embodiments shown and described in detail herein.

1. A computerized assessment method for providing current and futureproject status, the method comprising the steps of: generating, througha computer processor, a project ontology framework (POF) having aplurality of classes and subclasses, where each of said classes andsubclasses are linked to at least one other class or subclass; receivingdata into a memory connected to said computer processor; mapping datainto the POF, comprising the steps of: classifying said data in acomputer processor based on a source of said data; routing the data intoa memory based on its classification to at least one of a parsingfunction, a mapping function, and into echoes comprising data mappedinto the POF; and generating, through a computer processor, a statusmatrix on an output device connected to the computer from the datamapped into the project ontology framework by comparing the mapped datato data in a predetermined base POF, generating a match score, andmodifying an operational POF by incorporating the mapped data if a matchexceeds a certain threshold, the status matrix providing the current andfuture project status information including information about points ofweakness, wherein the classification step includes said computerprocessor classifying the data as a particular predetermined messagetype including a plurality of trusted parsed directed, trusted parsedprocessed, assigned confidence, no assigned confidence and fragment, andwherein the trusted parsed directed data is data that is already parsedand mapped to the POF, the trusted parsed processed data is data that isalready parsed, and the assigned confidence data, the no assignedconfidence data and the fragment data are data that has not been parsedor mapped, and wherein the trusted parsed directed data is routed intoechoes, bypassing the parsing function and the mapping function, thetrusted parsed processed directed data is routed to the mappingfunction, bypassing the parsing function, and the assigned confidencedata, the no assigned confidence data and the fragment data are routedto the parsing function.
 2. The method according to claim 1, furthercomprising parsing said data using natural language processing.
 3. Themethod according to claim 2, wherein said parsing step comprises parsingby tagging words of the data as to a part of speech, definition andtime.
 4. The method according to claim 3, wherein said parsing step usesa tree traversal parsing to tag the part of speech.
 5. The methodaccording to claim 3, further comprising evaluating and extracting thetagged words for mapping.
 6. The method according to claim 5, whereindata that includes the tagged words is formatted into a formatconsistent with at least one of the portfolio, the lexicon, the projecttemplate, the activity and the role classes or subclasses.
 7. The methodaccording to claim 6, further comprising generating an echo for eachpiece of formatted data encompassing the classes and subclasses of theproject ontology framework.
 8. The method according to claim 1, whereinsaid method is carried out to predict threat activities.
 9. A computerbased assessment method for providing current and future project status,the method comprising: generating a project ontology framework having aplurality of classes and subclasses, where each of said classes andsubclasses are linked to at least one other class or subclass; receivingintelligence data; classifying said data in a computer processor basedon a source of said data into a particular predetermined message typeincluding a plurality of trusted parsed directed, trusted parsedprocessed, assigned confidence, no assigned confidence and fragmentmessage type; routing the data based on said classification to at leastone of the parsing function, mapping function and the project ontologyframework; evaluating the data for extraction; extracting and formattingthe evaluated data for the classes and subclasses of the projectontology framework for mapping; generating echoes comprising data mappedinto of the project ontology framework using the formatted data and alearning algorithm that tests the formatted data against a base projectontology frame work and creates echoes based on matches; and generatinga status matrix on an output device which provides the current andfuture project status information based on one of the echoes of theproject ontology framework, wherein the trusted parsed directed data isdata that is already parsed and mapped to the project ontologyframework, the trusted parsed processed data is data that is alreadyparsed, and the assigned confidence data, the no assigned confidencedata and the fragment data are data that has not been parsed or mapped,and wherein the trusted parsed directed data is routed to the projectontology framework, bypassing the parsing function and the mappingfunction, the trusted parsed processed directed data is routed to themapping function bypassing the parsing function, and the assignedconfidence data, the no assigned confidence data and the fragment dataare routed to the parsing function.
 10. The method according to claim 9,wherein the data is formatted into a format consistent with at least oneof the portfolio, the lexicon, the project template, the activity, andthe role classes or subclasses.
 11. The method according to claim 9,wherein said generating echoes step comprises generating a candidate,working, or operational project ontology framework echoes based onweight and confidence cutoffs.
 12. The method according to claim 11,wherein the operational project ontology framework is the ontology withthe highest confidence level.
 13. The method according to claim 9,wherein said method is carried out to predict threat activities.
 14. Anassessment system comprising: a computer processor coupled to a projectassessment unit, the project assessment unit having: a project ontologyframework having a plurality of classes and subclasses, where each ofsaid classes and subclasses are linked to at least one other class orsubclass; a receiver for receiving intelligence data; a classifier ofsaid intelligence data based on a source of said data into a particularpredetermined message type; a router for routing said data based on saidclassification to at least one of a parsing function, mapping functionand the project ontology framework; an echo generator that generatesechoes comprising data mapped into the project ontology framework bymatching the intelligence data to data in a base project ontologyframework; a promoter that modifies an operational project ontologyframework if the echoes match the base project ontology framework to agreater degree than the operational project ontology framework; and amatrix generator for generating a status matrix which provides thecurrent and future project status information based on one of the echoesof the project ontology framework, wherein the predetermined messagetype is one of trusted parsed directed data comprising data that isalread arsed and mapped to the project ontology framework, trustedparsed processed data comprising data that is already parsed, andassigned confidence data, no assigned confidence data and fragment datacomprising data that has not been parsed or mapped, and wherein thetrusted parsed directed data is routed to the project ontologyframework, bypassing the parsing function and the mapping function, thetrusted parsed processed directed data is routed to the mappingfunction, bypassing the parsing function, and the assigned confidencedata, the no assigned confidence data, and the fragment data are routedto the parsing function.
 15. The system according to claim 14, whereinthe data is formatted into a format consistent with at least one of theportfolio, the lexicon, the project template, the activity and the roleclasses or subclasses.
 16. The system accordingly to claim 14, whereinsaid echo generator generates a candidate, working, or operationalproject ontology framework echoes based on weight and confidencecutoffs.
 17. The method according to claim 16, wherein the operationalproject ontology framework is the ontology with the highest confidencelevel.
 18. The method according to claim 14, wherein said system is usedto predict threat activities.
 19. A computer program, stored on acomputing device, for providing current and future project status, saidprogram comprising: a project ontology framework having a plurality ofclasses and subclasses, where each of said classes and subclasses arelinked to at least one other class or subclass; a receiver module,executed by the computing device, that receives data; a classificationmodule, executed by the computing device, that classifies said databased on a source of said data; a routing module, executed by thecomputing device, that routes the data based on its classification to atleast one of a parsing function, a mapping function, and into echoes; astate generation module, executed by the computing device, thatgenerates echoes of data mapped into the project ontology frameworkbased on matches between the data and a base project ontology framework;an echo maintenance module, executed by the computing device, thatmodifies an operational project ontology framework if the generatedechoes match the base project ontology framework to a greater degreethan the operational project ontology framework; and a matrix generationmodule, executed by the computing device, that generates a status matrixwhich provides the current and future project status informationincluding information about points of weakness based on one of theechoes of the project ontology framework, wherein the classificationmodule classifies data into predetermined message types includingtrusted parsed directed data comprising data that is already parsed andmapped to the project ontology framework trusted parsed processed datacorn rising data that is alread parsed, assigned confidence data, noassigned confidence data, and fragment data, and wherein the trustedparsed directed data is routed to into echoes, bypassing the parsingfunction and the mapping function, the trusted parsed processed directeddata is routed to the mapping function, bypassing the parsing function,and the assigned confidence data, the no assigned confidence data andthe fragment data are routed to the parsing function.